Tag Archives: examples

How AI is transforming local government

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By Steven McGinty

Last year, Scottish Local Government Chief Digital Officer Martyn Wallace spoke to the CIO UK podcast and highlighted that in 2019 local government must take advantage of artificial intelligence (AI) to deliver better outcomes for citizens. He explained:

“I think in the public sector we have to see AI as a way to deliver better outcomes and what I mean by that is giving the bots the grunt work – as one coworker called it, ‘shuffling spreadsheets’ – and then we can release staff to do the more complex, human-touch things.”

To date, very few councils have felt brave enough to invest in AI. However, the mood is slowly starting to change and there are several examples in the UK and abroad that show artificial intelligence is not just a buzzword, but a genuine enabler of change.

In December, Local Government Minister Rishi Sunak announced the first round of winners from a £7.5million digital innovation fund. The 16 winning projects, from 57 councils working in collaborative teams, were awarded grants of up to £100,000 to explore the use of a variety of digital technologies, from Amazon Alexa style virtual assistants to support people living in care, to the use of data analytics to improve education plans for children with special needs.

These projects are still in their infancy, but there are councils who are further along with artificial intelligence, and have already learned lessons and had measurable successes. For instance, Milton Keynes Council have developed a virtual assistant (or chatbot) to help respond to planning-related queries. Although still at the ‘beta’ stage, trials have shown that the virtual assistant is better able to validate major applications, as these are often based on industry standards, rather than household applications, which tend to be more wide-ranging.

Chief planner, Brett Leahy, suggests that introducing AI will help planners focus more on substantive planning issues, such as community engagement, and let AI “take care of the constant flow of queries and questions”.

In Hackney, the local council has been using AI to identify families that might benefit from additional support. The ‘Early Help Predictive System’ analyses data related to (among others) debt, domestic violence, anti-social behaviour, and school attendance, to build a profile of need for families. By taking this approach, the council believes they can intervene early and prevent the need for high cost support services. Steve Liddicott, head of service for children and young people at Hackney council, reports that the new system is identifying 10 or 20 families a month that might be of future concern. As a result, early intervention measures have already been introduced.

In the US, the University of Chicago’s initiative ‘Data Science for Social Good’ has been using machine learning (a form of AI) to help a variety of social-purpose organisations. This has included helping the City of Rotterdam to understand their rooftop usage – a key step in their goal to address challenges with water storage, green spaces and energy generation. In addition, they’ve also helped the City of Memphis to map properties in need of repair, enabling the city to create more effective economic development initiatives.

Yet, like most new technologies, there has been some resistance to AI. In December 2017, plans by Ofsted to use machine learning tools to identify poorly performing schools were heavily criticised by the National Association of Head Teachers. In their view, Ofsted should move away from a data-led approach to inspection and argued that it was important that the “whole process is transparent and that schools can understand and learn from any assessment.”

Further, hyperbole-filled media reports have led to a general unease that introducing AI could lead to a reduction in the workforce. For example, PwC’s 2018 ‘UK Economic Outlook’ suggests that 18% of public administration jobs could be lost over the next two decades. Although its likely many jobs will be automated, no one really knows how the job market will respond to greater AI, and whether the creation of new jobs will outnumber those lost.

Should local government investment in AI?

In the next few years, it’s important that local government not only considers the clear benefits of AI, but also addresses the public concerns. Many citizens will be in favour of seeing their taxes go further and improvements in local services – but not if this infringes on their privacy or reduces transparency. Pilot projects, therefore, which provide the opportunity to test the latest technologies, work through common concerns, and raise awareness among the public, are the best starting point for local councils looking to move forward with this potentially transformative technology.

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Crowdsourcing in smart cities: a world of best practice

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By Steven McGinty

Too often, debates on smart cities revolve around terms such as “Internet of things”, “big data”, and “sensors”. However, there is a growing realisation that truly smart cities take a more person-centric approach, which focuses on the needs of citizens and harnesses their skills, talents and experience.

Crowdsourcing is one approach that can help cities do just that. From Danish toy maker Lego to tech giant Amazon, organisations are using digital tools to gather views, opinions, data, and even money from citizens. Public sector institutions have also got involved, introducing projects that engage with citizens, as well as tap into external skills through events such as hackathons (where civic hackers come together to solve key city problems).

Already, there is a wide range of crowdsourcing initiatives across the world. Below I’ve highlighted some of the best.

Scottish Government

In 2015, the Scottish Government’s Open Data and Fisheries teams introduced Dialogue, a citizen engagement tool developed by Delib (a social enterprise based in the UK and Australia).

The Open Data team were in the process of creating an open data plan for public bodies. They felt that crowdsourcing could help them gain a greater understanding of the types and formats of datasets people would be interested in, and as such, posed a series of questions to citizens.

The Fisheries Team took to crowdsourcing to gather the views on a proposal to create a ‘kill licence’ and carcass tagging regime for salmon. As they knew this would be controversial, they wanted to gain a better understanding of the concerns in fishing communities, and to see if there were any better approaches.

Both teams learned a lot of useful lessons from the process. These included:

  • ensuring questions were as specific as possible so citizens could understand;
  • marketing projects to specific communities with an interest in the question raised;
  • avoiding making assumptions or stereotyping audiences; and
  • giving short deadlines (as this added urgency and encouraged greater participation).

Milton Keynes

MK: Smart – Milton Keynes’ wide ranging smart cities programme – has introduced an online platform known as Our MK to connect with citizens. This award-winning project supports people in playing a central role in urban innovation, from crowdsourcing initial ideas through to finding mentoring support and funding through their dedicated SpaceHive page.

The platform’s citizen ideas competition offers up to £5,000 worth of funding to turn ideas into reality. So far it’s generated over 100 ideas, with 13 projects being allocated funding. This includes the Go Breastfeeding MK App (an app which promotes the use of breastfeeding within Milton Keynes) and the gamification of Redways (which saw an app developed to encourage people to explore the Redways network – a series of shared use paths for cyclists and pedestrians.)

Madrid City Council

In 2016, Madrid City Council launched Decide Madrid. The platform played a key role in supporting the city’s participatory budgeting process, allowing citizens to propose, debate, and rank ideas submitted to the website. Once citizens had chosen their top proposals, city employees checked the ideas against viability criteria and a cost report was carried out. If the proposal failed to meet the criteria, a report was published explaining why it had been excluded.

Decide Madrid provided guidance of what was allowed and what was not (offline meetings were also used to explain the limitations of the scheme), to ensure that only valid proposals were checked. This ensured the initiative didn’t become too labour intensive.

In the 2016 Budget, €60 million was set aside. By the time the process had finished, citizens had debated over 5,000 initial ideas, with 225 projects being chosen for funding.

Reykjavik City Council

Better Reykjavik was introduced to provide a direct link for citizens to Reykjavik City Council. The online platform enables citizens to voice, debate and prioritise the issues that they believe will improve their city. For example, Icelandic school children have suggested the need for more field trips.

In 2010, the platform played an important role in Reykjavik’s city council elections, providing a space for all political parties to crowdsource ideas for their campaign. After the election, Jón Gnarr, former Mayor of Reykjavik, encouraged citizens to use the platform during coalition talks. Within a four week period (before and after the election), 40% of Reykjavik’s voters had used the platform and almost 2000 priorities had been created.

Overall, almost 60% of citizens have used the platform, and the city has spent approximately £1.7 million on developing projects sourced from citizens.

Final thoughts

Crowdsourcing is more than just creating a flashy website or app. It’s a process which requires strategic planning and investment. If you’re planning your own initiative, seeking out good practice and learning from the experience of others is a great place to start.

This article was based on the briefing ‘The crowdsourced city: engaging citizens in smart cities’. Idox Information Service members can access this briefing via our customer website.